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A teaching tool about the fickle p value and other statistical principles based on real-life data.

Salem AlawbathaniMehreen BatoolJan FleckhausSarkawt HamadFloyd HassenrückYanhong HouXia LiJon Salmanton-GarcíaSami UllahFrederique WietersMartin Christian Michel
Published in: Naunyn-Schmiedeberg's archives of pharmacology (2021)
A poor understanding of statistical analysis has been proposed as a key reason for lack of replicability of many studies in experimental biomedicine. While several authors have demonstrated the fickleness of calculated p values based on simulations, we have experienced that such simulations are difficult to understand for many biomedical scientists and often do not lead to a sound understanding of the role of variability between random samples in statistical analysis. Therefore, we as trainees and trainers in a course of statistics for biomedical scientists have used real data from a large published study to develop a tool that allows scientists to directly experience the fickleness of p values. A tool based on a commonly used software package was developed that allows using random samples from real data. The tool is described and together with the underlying database is made available. The tool has been tested successfully in multiple other groups of biomedical scientists. It can also let trainees experience the impact of randomness, sample sizes and choice of specific statistical test on measured p values. We propose that live exercises based on real data will be more impactful in the training of biomedical scientists on statistical concepts.
Keyphrases
  • electronic health record
  • big data
  • molecular dynamics
  • data analysis
  • randomized controlled trial
  • primary care
  • adverse drug
  • neural network
  • medical education